Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

9200 participants

Study Classification

INTERVENTIONAL

Study Start Date

2023-05-31

Study Completion Date

2027-12-31

Brief Summary

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Predicting surgical risks are important to patients and clinicians for shared decision making process and management plan. The study team aim to conduct a hybrid type 1 effectiveness implementation study design. A Randomized Controlled Trial where participants undergoing surgery In Singapore General Hospital (SGH) will be allocated in 1:1 ratio to CARES-guided (unblinded to risk level) or to unguided (blinded to risk level) groups. All participants undergoing elective surgeries in SGH will be considered eligible for enrolment into the study. For elective surgeries, the participants will mainly be recruited from Pre-admission Centre. The outcome of this study will help patients and clinicians make better decisions together. Firstly, the deployment of the CARES model in a live clinical environment could potentially reduce postoperative complications and improve the quality of surgical care provision. The findings from this study would allow fine-tuning of CARES as well as further deployment of additional risk models for specific complications other than Mortality and ICU stay. This in turn would translate to better health for the surgical population and improved cost-effectiveness. This is significant as the surgical population is expected to continuously grow due to improved access to care, better technologies and the aging population. Secondly, IMAGINATIVE will be instrumental in improving our understanding of the deployment strategies for AI/ML predictive models in healthcare. Models such as CARES could be the standard of care in the future if proven to improve the health outcomes of patients. As model deployments are costly and can be disruptive to the EMR processes, this study would be the initial spark for future deployment and health services research focusing on improving the value of these model deployments.

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Detailed Description

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Conditions

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Machine Learning

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

OTHER

Blinding Strategy

NONE

Study Groups

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CARES-guided Group

The Intervention

Group Type ACTIVE_COMPARATOR

CARES-guided Group

Intervention Type OTHER

Participants randomised to the CARES-guided arm will have their CARES-score calculated and entered into the Pre-Anesthesia Assessment electronic form within the Electronic Medical Records (EMR). This score and its relevant advisories will be prominently displayed on this electronic form. (Participants on this arm will receive this intervention in addition to the routine practice).

Non CARES-Guided Group

The control - Participants randomized to the control arm will continue to have their routine Pre-Anesthesia Assessment on the electronic form, without the CARES calculator calculations, as per current practice

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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CARES-guided Group

Participants randomised to the CARES-guided arm will have their CARES-score calculated and entered into the Pre-Anesthesia Assessment electronic form within the Electronic Medical Records (EMR). This score and its relevant advisories will be prominently displayed on this electronic form. (Participants on this arm will receive this intervention in addition to the routine practice).

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

1. Patients \>=21 Years old
2. Patients going for elective surgery

For semi-structured interview:

1\. Any clinician or nurse that used CARES during the research trial